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Reseach Article

Facial Expression Detection and Recognition using VGG- 16

by Aradhana Singh Parihar, Shweta Agrawal
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 41
Year of Publication: 2021
Authors: Aradhana Singh Parihar, Shweta Agrawal
10.5120/ijca2021921803

Aradhana Singh Parihar, Shweta Agrawal . Facial Expression Detection and Recognition using VGG- 16. International Journal of Computer Applications. 183, 41 ( Dec 2021), 9-16. DOI=10.5120/ijca2021921803

@article{ 10.5120/ijca2021921803,
author = { Aradhana Singh Parihar, Shweta Agrawal },
title = { Facial Expression Detection and Recognition using VGG- 16 },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2021 },
volume = { 183 },
number = { 41 },
month = { Dec },
year = { 2021 },
issn = { 0975-8887 },
pages = { 9-16 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume183/number41/32201-2021921803/ },
doi = { 10.5120/ijca2021921803 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:19:21.250377+05:30
%A Aradhana Singh Parihar
%A Shweta Agrawal
%T Facial Expression Detection and Recognition using VGG- 16
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 41
%P 9-16
%D 2021
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Facial expression recognition software is useful in a variety of circumstances. In recent years, there has been a lot of research on facial expression detection and recognition. Facial expression recognition software is useful in a variety of circumstances such as, security, camera surveillance, criminal investigations, smart card applications, database management systems, and in modern devices for identity verification etc. This paper shows how to implement facial expression detection and recognition system. The facial recognition is to recognize and validate facial traits. However, Haar cascade detection is used to captured facial features in real time. In three different phases the sequential process work can be define, In the first step, a camera detects a human face, and the acquired input is processed based on features with the help of the Keras convolutional neural network model database. Human faces are validated in the third stage to categorize human emotions as happy, neutral, furious, sad, and surprised. This suggested study is broken down into two aspirations: face detection and expression identification. This work will comes under computer vision field. We will use opencv, keras and python programming in this project. The testing result illustrates the system's perfection in detecting and recognizing facial expressions. Finally, we will be able to obtain accurate facial expression detection and identification results.

References
  1. B. Fasel and J. Luettin, "Automatic facial expression analysis: a survey," Pattern Recognition, vol. 36, no. 1, pp. 259-275, 2003.
  2. P. Ekman and E.L. Rosenberg, What the face reveals: Basic and applied studies of spontaneous expression using the Facial Action Coding System. USA: Oxford University Press, 1997.
  3. C. Juanjuan, Z. Zheng, S. Han, and Z. Gang, "Facial expression recognition based on PCA reconstruction," in International Conference on Computer Science and Education, 2010, pp. 195-198.
  4. I. Cohen, N. Sebe, A. Garg, L.S. Chen, and T.S. Huang, "Facial expression recognition from video sequences: temporal and static modeling," Computer Vision and Image Understanding, vol. 91, no. 1, pp. 160-187, 2003.
  5. Agrawal, Shweta & Jain, Sanjiv. (2020). Medical Text and Image Processing: Applications, Issues and Challenges. Springer Nature 10.1007/978-3-030-40850-3_11.
  6. Shweta Agrawal, S. Jain, et al. “An orchestrator for networked control system and its application to collision avoidance in multiple mobile robots, International Journal of Engineering Systems Modelling and Simulation, 2021 Vol.12 No.2/3, pp.103 - 110
  7. Manoj Agrawal, Shweta Agrawal “Rice plant diseases detection classification using deep learning models: a systematic review” Journal of critical reviews JCR. 2020; vol. 7 issue: 11: 4376-4390
  8. M. Agrawal, S. Agrawal, “A Systematic Review on Artificial Intelligence/Deep Learning Applications and Challenges to battle against COVID-19 Pandemic” Disaster Advances, Vol. 14(8) August 2021, pp90-99.
  9. R. Tandon, S. Agrawal, “Sequential CNN for automatic breast cancer detection using histopathological images” Journal of critical reviews JCR 2020, Vol 7 issue 15: 6104-6117.
  10. D. Saravagi, S. Agrawal “Opportunities and challenges of ML model for prediction and diagnosis of spondylolithesis: A systematic review International Journal of Engineering Systems Modelling and Simulation, 2021 Vol.12 No.2/3, pp.127 - 138 (Scopus indexed)
  11. D. Saravagi, S. Agrawal “Indian Stock Market analysis and prediction using the LSTM model during COVID-19” International Journal of Engineering Systems Modelling and Simulation, 2021 Vol.12 No.2/3, pp.139 - 147 (Scopus indexed)
  12. M. Agrawal, S. Agrawal “Rice Plant Diseases Detection Using Convolutional Neural Networks” International Journal of Engineering Systems Modelling and Simulation, Accepted for publication (Scopus indexed)
  13. S. Jain, S. Agrawal “A Decision Tree C4.5 based Voltage Security Events Classifier for Electric Power Systems” International Journal of Engineering Systems Modelling and Simulation, Accepted for publication (Scopus indexed)
  14. Y. Wang, H. Ai, B. Wu, and C. Huang, "Real time facial expression recognition with adaboost," in Proceedings of the 17th International Conference on Pattern Recognition, 2004, pp. 926-929.
  15. D.T. Lin, "Facial expression classification using PCA and hierarchical radial basis function network," Journal of information science and engineering, vol. 22, no. 5, pp. 1033-1046, 2006.
  16. I.A. Essa and A.P. Pentland, "Coding, analysis, interpretation, and recognition of facial expressions," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 757-763, 1997.
  17. K. Anderson and P.W. McOwan, "A Real-Time Automated System for the Recognition of human facial expressions," IEEE Transactions on Systems, Man, and Cybernetics, vol. 36, no. 1, pp. 96-105, 2006.
  18. P. Kakumanu and N. Bourbakis, "A local-global graph approach for facial expression recognition," in IEEE International Conference on Tools with Artificial Intelligence, 2006, pp. 685-692.
  19. Shaik Asif Hussain, Ahlam Salim Abdallah al Balushi."A real time face emotion classification and recognition using deep learning model",journal of physics: conference series, 2020, publication.
Index Terms

Computer Science
Information Sciences

Keywords

Facial Expression Detection VGG- 16